人工智能学院孟繁托同学以第一作者身份撰写的科研论文被中科院SCI二区期刊Solar Energy期刊接收。Solar Energy 是Elsevier 出版社旗下的中科院SCI二区期刊，该期刊2022年影响因子：6.7。
孟繁托同学是王献昌老师2019级博士研究生，一直从事于能源预测与调度的研究工作。本篇工作是与Stanford University 斯坦福大学Tyler lum博士合作完成。
论文题目：Digital Twin for Intelligent Probabilistic Short Term Load Forecasting in Solar Based Smart Grids using Shark Algorithm
This article proposes a novel evolving based prediction model for the accurate short term load forecasting in solar based smart grids. The proposed method uses a probabilistic method for uncertainty quantization to make sure that the maximum modeling of the prediction interval would be achieved in a renewable based environment. In this regard, the innovative lower upper bound estimation method(LUBE) is trained using the real-time data of the smart grid gathered by the digital twin of the system. This would result in much higher results due to the avoidance of malfunction of the smart metering devices located within the smart grid. Digital twin can help predict the load demand in solar based smart grids by using machine learning algorithms to analyze the data from the smart grid. This data can be used to create a model of the system and predict how the load demand will change based on different factors such as weather, time of day, and season. By understanding the load demand,solar based smart grids can better manage their energy resources and optimize the performance of the system. In order to improve the model performance, white shark optimization algorithm (WSOA) is used as the trainer of the prediction model in a heuristic environment. The results advocate the high accuracy and reliability of the proposed method on practical dataset.